arXiv subjects

Sheekar Banerjee

Publications and source records attributed to Sheekar Banerjee.

4 recordsLinked to original sources

arXiv2025

A Novel Approach for a Smart IoMT-Based BAN for an Old Home Healthcare Monitoring System Using Starlink

The rapid evolution of the Internet of Medical Things (IoMT) technology has become a transformative force in modern healthcare, particularly in elderly patient management. The current elderly care system faces significant challenges, including insufficient long-term care resources and poor communication between healthcare providers. To address this limitation, this study introduces a novel Starlink-assisted IOMT-based elderly healthcare model designed to improve remote patient monitoring and communication reliability. This proposal system focused on a monitoring system of key biomedical parameters such as electrocardiogram (ECG), body temperature, heart attack indicators, and a fall detection alert system. Performance is evaluated using the network simulator (NS-3) to assess its effectiveness in remote and underserved regions. Physiological data collected from patients are transmitted through a local communication hub and forwarded over a Low Earth Orbit (LEO) satellite link to a medical center. Based on Quality of Service (QoS) technology that combines Flow Queuing (FQ) with Controlled Delay (CoDel) with Differentiated Services Code Point (DSCP) marking. This approach prioritizes critical health data for faster transmission while allocating lower priority to non-urgent information. This architecture is entirely wireless, allowing continuous monitoring, real-time alerts, and secure data storage for medical analysis. The simulation results demonstrate that the proposed Starlink-enabled IOMT system outperforms existing solutions in terms of throughput, latency, and reliability. The findings highlight Starlink's potential as a robust and high-performance telehealth communication tool. In addition, this study provides a scalable and reproducible framework for future satellite-assisted healthcare systems that prioritize quality of service (QoS) performance and improved elderly patient care.

cs.NI↗

arXiv2025

Squeezed-Eff-Net: Edge-Computed Boost of Tomography Based Brain Tumor Classification leveraging Hybrid Neural Network Architecture

Brain tumors are one of the most common and dangerous neurological diseases which require a timely and correct diagnosis to provide the right treatment procedures. Even with the promotion of magnetic resonance imaging (MRI), the process of tumor delineation is difficult and time-consuming, which is prone to inter-observer error. In order to overcome these limitations, this work proposes a hybrid deep learning model based on SqueezeNet v1 which is a lightweight model, and EfficientNet-B0, which is a high-performing model, and is enhanced with handcrafted radiomic descriptors, including Histogram of Oriented Gradients (HOG), Local Binary Patterns (LBP), Gabor filters and Wavelet transforms. The framework was trained and tested only on publicly available Nickparvar Brain Tumor MRI dataset, which consisted of 7,023 contrast-enhanced T1-weighted axial MRI slices which were categorized into four groups: glioma, meningioma, pituitary tumor, and no tumor. The testing accuracy of the model was 98.93% that reached a level of 99.08% with Test Time Augmentation (TTA) showing great generalization and power. The proposed hybrid network offers a compromise between computation efficiency and diagnostic accuracy compared to current deep learning structures and only has to be trained using fewer than 2.1 million parameters and less than 1.2 GFLOPs. The handcrafted feature addition allowed greater sensitivity in texture and the EfficientNet-B0 backbone represented intricate hierarchical features. The resulting model has almost clinical reliability in automated MRI-based classification of tumors highlighting its possibility of use in clinical decision-support systems.

cs.CV↗

arXiv2025

Underwater Image Reconstruction Using a Swin Transformer-Based Generator and PatchGAN Discriminator

Underwater imaging is essential for marine exploration, environmental monitoring, and infrastructure inspection. However, water causes severe image degradation through wavelength-dependent absorption and scattering, resulting in color distortion, low contrast, and haze effects. Traditional reconstruction methods and convolutional neural network-based approaches often fail to adequately address these challenges due to limited receptive fields and inability to model global dependencies. This paper presented a novel deep learning framework that integrated a Swin Transformer architecture within a generative adversarial network (GAN) for underwater image reconstruction. Our generator employed a U-Net structure with Swin Transformer blocks to capture both local features and long-range dependencies crucial for color correction across entire images. A PatchGAN discriminator provided adversarial training to ensure high-frequency detail preservation. We trained and evaluated our model on the EUVP dataset, which contains paired underwater images of varying quality. Quantitative results demonstrate stateof-the-art performance with PSNR of 24.76 dB and SSIM of 0.89, representing significant improvements over existing methods. Visual results showed effective color balance restoration, contrast improvement, and haze reduction. An ablation study confirms the superiority of our Swin Transformer designed over convolutional alternatives. The proposed method offers robust underwater image reconstruction suitable for various marine applications.

cs.CV↗

arXiv2023

CEIMVEN: An Approach of Cutting Edge Implementation of Modified Versions of EfficientNet (V1-V2) Architecture for Breast Cancer Detection and Classification from Ultrasound Images

Undoubtedly breast cancer identifies itself as one of the most widespread and terrifying cancers across the globe. Millions of women are getting affected each year from it. Breast cancer remains the major one for being the reason of largest number of demise of women. In the recent time of research, Medical Image Computing and Processing has been playing a significant role for detecting and classifying breast cancers from ultrasound images and mammograms, along with the celestial touch of deep neural networks. In this research, we focused mostly on our rigorous implementations and iterative result analysis of different cutting-edge modified versions of EfficientNet architectures namely EfficientNet-V1 (b0-b7) and EfficientNet-V2 (b0-b3) with ultrasound image, named as CEIMVEN. We utilized transfer learning approach here for using the pre-trained models of EfficientNet versions. We activated the hyper-parameter tuning procedures, added fully connected layers, discarded the unprecedented outliers and recorded the accuracy results from our custom modified EfficientNet architectures. Our deep learning model training approach was related to both identifying the cancer affected areas with region of interest (ROI) techniques and multiple classifications (benign, malignant and normal). The approximate testing accuracies we got from the modified versions of EfficientNet-V1 (b0- 99.15%, b1- 98.58%, b2- 98.43%, b3- 98.01%, b4- 98.86%, b5- 97.72%, b6- 97.72%, b7- 98.72%) and EfficientNet-V2 (b0- 99.29%, b1- 99.01%, b2- 98.72%, b3- 99.43%) are showing very bright future and strong potentials of deep learning approach for the successful detection and classification of breast cancers from the ultrasound images at a very early stage. The code for this research is available here: https://github.com/ac005sheekar/CEIMVEN-Breast.

eess.IV↗